Tradeoff Between Area Coverage and Energy Usage of a Self-Reconfigurable Floor Cleaning Robot Based on User Preference

Design and Experiment of a Novel Façade Cleaning Robot with a Biped Mechanism

Applied Sciences ◽

10.3390/app8122398 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2398 ◽

Cited By ~ 3

Author(s):

Shunsuke Nansai ◽

Keichi Onodera ◽

Prabakaran Veerajagadheswar ◽

Mohan Rajesh Elara ◽

Masami Iwase

Keyword(s):

Polynomial Interpolation ◽

Area Coverage ◽

Glass Panel ◽

High Rise ◽

Cleaning Robot ◽

Glass Panels ◽

Glass Facade ◽

Cleaning Robots ◽

Real Scenario ◽

Glass Façade

Façade cleaning in high-rise buildings has always been considered a hazardous task when carried out by labor forces. Even though numerous studies have focused on the development of glass façade cleaning systems, the available technologies in this domain are limited and their performances are broadly affected by the frames that connect the glass panels. These frames generally act as a barrier for the glass façade cleaning robots to cross over from one glass panel to another, which leads to a performance degradation in terms of area coverage. We present a new class of façade cleaning robot with a biped mechanism that is able overcome these obstacles to maximize its area coverage. The developed robot uses active suction cups to adhere to glass walls and adopts mechanical linkage to navigate the glass surface to perform cleaning. This research addresses the design challenges in realizing the developed robot. Its control system consists of inverse kinematics, a fifth polynomial interpolation, and sequential control. Experiments were conducted in a real scenario, and the results indicate that the developed robot achieves significantly higher coverage performance by overcoming both negative and positive obstacles in a glass panel.

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Tackling Area Coverage Problems in a Reconfigurable Floor Cleaning Robot Based on Polyomino Tiling Theory

Applied Sciences ◽

10.3390/app8030342 ◽

2018 ◽

Vol 8 (3) ◽

pp. 342 ◽

Cited By ~ 7

Author(s):

Veerajagadheswar Prabakaran ◽

Rajesh Mohan ◽

Vinu Sivanantham ◽

Thejus Pathmakumar ◽

Suganya Kumar

Keyword(s):

Area Coverage ◽

Cleaning Robot ◽

Coverage Problems

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Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot

Sensors ◽

10.3390/s21041067 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1067 ◽

Cited By ~ 2

Author(s):

Koppaka Ganesh Sai Apuroop ◽

Anh Vu Le ◽

Mohan Rajesh Elara ◽

Bing J. Sheu

Keyword(s):

Reinforcement Learning ◽

Short Term Memory ◽

Learning Algorithm ◽

Area Coverage ◽

Current Paper ◽

Coverage Problem ◽

Entire Area ◽

Maximum Area ◽

Coverage Path Planning ◽

Cleaning Robot

One of the essential attributes of a cleaning robot is to achieve complete area coverage. Current commercial indoor cleaning robots have fixed morphology and are restricted to clean only specific areas in a house. The results of maximum area coverage are sub-optimal in this case. Tiling robots are innovative solutions for such a coverage problem. These new kinds of robots can be deployed in the cases of cleaning, painting, maintenance, and inspection, which require complete area coverage. Tiling robots’ objective is to cover the entire area by reconfiguring to different shapes as per the area requirements. In this context, it is vital to have a framework that enables the robot to maximize the area coverage while minimizing energy consumption. That means it is necessary for the robot to cover the maximum area with the least number of shape reconfigurations possible. The current paper proposes a complete area coverage planning module for the modified hTrihex, a honeycomb-shaped tiling robot, based on the deep reinforcement learning technique. This framework simultaneously generates the tiling shapes and the trajectory with minimum overall cost. In this regard, a convolutional neural network (CNN) with long short term memory (LSTM) layer was trained using the actor-critic experience replay (ACER) reinforcement learning algorithm. The simulation results obtained from the current implementation were compared against the results that were generated through traditional tiling theory models that included zigzag, spiral, and greedy search schemes. The model presented in the current paper was also compared against other methods where this problem was considered as a traveling salesman problem (TSP) solved through genetic algorithm (GA) and ant colony optimization (ACO) approaches. Our proposed scheme generates a path with a minimized cost at a lesser time.

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Multi-Criteria Decision Making for Efficient Tiling Path Planning in a Tetris-Inspired Self-Reconfigurable Cleaning Robot

Applied Sciences ◽

10.3390/app9010063 ◽

2018 ◽

Vol 9 (1) ◽

pp. 63 ◽

Cited By ~ 5

Author(s):

Maryam Kouzehgar ◽

Mohan Rajesh Elara ◽

Mahima Ann Philip ◽

Manimuthu Arunmozhi ◽

Veerajagadheswar Prabakaran

Keyword(s):

Decision Making ◽

Energy Consumption ◽

Path Planning ◽

Robot Navigation ◽

Current Approach ◽

Area Coverage ◽

Preference Order ◽

Multi Criteria Decision Making ◽

Cleaning Robot ◽

Furniture Layout

In this study, we aim to optimize and improve the efficiency of a Tetris-inspired reconfigurable cleaning robot. Multi-criteria decision making (MCDM) is utilized as a powerful tool to target this aim by introducing the best solution among others in terms of lower energy consumption and greater area coverage. Regarding the Tetris-inspired structure, polyomino tiling theory is utilized to generate tiling path-planning maps which are evaluated via MCDM to seek a solution that can deliver the best balance between the two mentioned key issues; energy and area coverage. In order to obtain a tiling area that better meets the requirements of polyomino tiling theorems, first, the whole area is decomposed into five smaller sub-areas based on furniture layout. Afterward, four tetromino tiling theorems are applied to each sub-area to give the tiling sets that govern the robot navigation strategy in terms of shape-shifting tiles. Then, the area coverage and energy consumption are calculated and eventually, these key values are considered as the decision criteria in a MCDM process to select the best tiling set in each sub-area, and following the aggregation of best tiling path-plannings, the robot navigation is oriented towards efficiency and improved optimality. Also, for each sub-area, a preference order for the tiling sets is put forward. Based on simulation results, the tiling theorem that can best serve all sub-areas turns out to be the same. Moreover, a comparison between a fixed-morphology mechanism with the current approach further advocates the proposed technique.

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Toward Pleomorphic Reconfigurable Robots for Optimum Coverage

Complexity ◽

10.1155/2021/3705365 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

S. M. Bhagya P. Samarakoon ◽

M. A. Viraj J. Muthugala ◽

Mohan R. Elara ◽

Selva kumaran

Keyword(s):

Maximum Size ◽

Area Coverage ◽

Optimum Size ◽

Performance Factors ◽

Reconfigurable Robots ◽

Cleaning Robot ◽

Cleaning Robots ◽

Novel Concept ◽

Industrial Needs ◽

And Behavior

Buildings are constructed for accommodating living and industrial needs. Floor cleaning robots have been developed to cater to the demand of these buildings. Area coverage and coverage time are crucial performance factors of a floor cleaning robot. Reconfigurable tiling robots have been introduced over fixed shape robots to improve area coverage in floor cleaning applications compared to robots with fixed morphologies. However, area coverage and coverage time of a tiling robot compromised one another. This study proposes a novel concept that considers the ability of a tiling robot to configure both its morphology and size according to the environment. This concept is inspired by the pleomorphism that could be seen in bacteria. In this regard, P-hTetro, a pleomorphic tiling robot that can reconfigure its morphology and size, is considered. A novel coverage strategy for realizing the size reconfiguration is also proposed. According to this strategy, the robot covers obstacle-free areas with its maximum size, while an obstacle cluster is covered after shrinking to an optimum size. The optimum size for reconfiguration is determined by the genetic algorithm based on the arrangement of the environment. The performance and behavior of the proposed P-hTetro have been compared against that of an existing tiling robot which has a fixed size. According to the statistical outcomes, a tiling robot with the ability to reconfigure its size can significantly improve the performance in the aspects of area coverage and coverage time compared to a tiling robot with no ability to reconfigure its size.

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Ensuring Area Coverage and Safety of a Reconfigurable Staircase Cleaning Robot

IEEE Access ◽

10.1109/access.2021.3125192 ◽

2021 ◽

pp. 1-1

Author(s):

M. A. Viraj J. Muthugala ◽

S. M. Bhagya P. Samarakoon ◽

Prabakaran Veerajagadheswar ◽

Mohan Rajesh Elara

Keyword(s):

Area Coverage ◽

Cleaning Robot

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Toward complete area coverage of a reconfigurable tiling robot by following obstacle shape

Complex & Intelligent Systems ◽

10.1007/s40747-020-00243-3 ◽

2021 ◽

Author(s):

S. M. Bhagya P. Samarakoon ◽

M. A. Viraj J. Muthugala ◽

Anh Vu Le ◽

Mohan Rajesh Elara

Keyword(s):

Genetic Algorithm ◽

Area Coverage ◽

Experimental Results ◽

Coverage Problem ◽

Cluttered Environments ◽

Cleaning Robot ◽

Novel Method

AbstractComplete area coverage is a crucial factor for a floor cleaning robot. Self-reconfigurable tiling robots have been introduced over robots with a fixed shape for floor cleaning since they improve the area coverage by the flexibility of shape-shifting in cluttered environments. The existing coverage methods of reconfigurable tiling robots follow the tiling theory to cope with the area coverage problem. However, these methods merely consider a limited set of predefined shapes for the reconfiguration of a robot. The consideration of a limited set of predefined shapes for the reconfiguration impedes the ability of coverage to a certain extent in typical floor environments. Therefore, this paper proposes a novel method to improve area coverage of a tiling robot by reconfiguring according to the shape of obstacles. To this end, the required hinge angles for reconfiguring per the shape of an obstacle are determined by a genetic algorithm. The proposed method considers an optimized shape for reconfiguration in lieu of a limited set of predefined shapes. The coverage improvement of the proposed concept has been compared against the existing coverage methods of tiling robots to validate the performance. According to the experimental results, the proposed method surpasses the existing coverage methods of tiling robots from the perspective of area coverage, and the improvement is significant and noteworthy.

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